Chapter 6 Diversity analysis

6.1 Alpha diversity

# Calculate Hill numbers
richness <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 0) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(richness = 1) %>%
  rownames_to_column(var = "sample")

neutral <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(neutral = 1) %>%
  rownames_to_column(var = "sample")

phylogenetic <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, tree = genome_tree) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(phylogenetic = 1) %>%
  rownames_to_column(var = "sample")

# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
  to.elements(., GIFT_db) %>%
  traits2dist(., method = "gower")

functional <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, dist = dist) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(functional = 1) %>%
  rownames_to_column(var = "sample") %>%
  mutate(functional = if_else(is.nan(functional), 1, functional))

# Merge all metrics
alpha_div <- richness %>%
  full_join(neutral, by = join_by(sample == sample)) %>%
  full_join(phylogenetic, by = join_by(sample == sample)) %>%
  full_join(functional, by = join_by(sample == sample))

6.1.1 Wild samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.2 Acclimation samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.3 Antibiotics samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="2_Antibiotics") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.4 Transplant_1 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="3_Transplant1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.5 Transplant_2 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="4_Transplant2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.6 Post-Transplant_1 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.7 Post-Transplant_2 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.2 Beta diversity

beta_q0n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, tree = genome_tree)

beta_q1f <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, dist = dist)

6.3 Permanovas

6.3.0.1 Load required data

meta <- column_to_rownames(sample_metadata, "Tube_code")

6.3.1 1. Are the wild populations similar?

6.3.1.1 Wild: P.muralis vs P.liolepis

wild <- meta %>%
  filter(time_point == "0_Wild")

# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

wild.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(wild))]
identical(sort(colnames(wild.counts)), sort(as.character(rownames(wild))))

wild_nmds <- sample_metadata %>%
  filter(time_point == "0_Wild")

6.3.1.2 Number of samples used

[1] 27
beta_div_richness_wild<-hillpair(data=wild.counts, q=0)
beta_div_neutral_wild<-hillpair(data=wild.counts, q=1)
beta_div_phylo_wild<-hillpair(data=wild.counts, q=1, tree=genome_tree)
beta_div_func_wild<-hillpair(data=wild.counts, q=1, dist=dist)

6.3.1.3 Richness


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.000012 0.000012 0.0012    999  0.974
Residuals 25 0.257281 0.010291                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                  Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis                              0.974
Podarcis_muralis            0.97302                 
Df SumOfSqs R2 F Pr(>F)
species 1 1.542719 0.2095041 6.625717 0.001
Residual 25 5.820951 0.7904959 NA NA
Total 26 7.363669 1.0000000 NA NA

6.3.1.4 Neutral


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.000048 0.0000476 0.0044    999  0.951
Residuals 25 0.270114 0.0108046                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                  Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis                              0.956
Podarcis_muralis            0.94763                 
Df SumOfSqs R2 F Pr(>F)
species 1 1.918266 0.2608511 8.822682 0.001
Residual 25 5.435610 0.7391489 NA NA
Total 26 7.353876 1.0000000 NA NA

6.3.1.5 Phylogenetic


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.03585 0.035847 2.4912    999  0.131
Residuals 25 0.35973 0.014389                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                  Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis                              0.132
Podarcis_muralis            0.12705                 
Df SumOfSqs R2 F Pr(>F)
species 1 0.3218613 0.2162815 6.899207 0.001
Residual 25 1.1662981 0.7837185 NA NA
Total 26 1.4881594 1.0000000 NA NA

6.3.1.6 Functional


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.018367 0.018367 1.5597    999  0.241
Residuals 25 0.294402 0.011776                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                  Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis                               0.24
Podarcis_muralis            0.22328                 
Df SumOfSqs R2 F Pr(>F)
species 1 0.0858578 0.172879 5.225323 0.054
Residual 25 0.4107775 0.827121 NA NA
Total 26 0.4966352 1.000000 NA NA
beta_q0n_nmds_wild <- beta_div_richness_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

beta_q1n_nmds_wild <- beta_div_neutral_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

beta_q1p_nmds_wild <- beta_div_phylo_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

beta_q1f_nmds_wild <- beta_div_func_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

6.3.2 2. Effect of acclimation

accli <- meta %>%
  filter(time_point == "1_Acclimation")

# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

accli.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(accli))]
identical(sort(colnames(accli.counts)), sort(as.character(rownames(accli))))

accli_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation")

6.3.2.1 Number of samples used

[1] 27
beta_div_richness_accli<-hillpair(data=accli.counts, q=0)
beta_div_neutral_accli<-hillpair(data=accli.counts, q=1)
beta_div_phylo_accli<-hillpair(data=accli.counts, q=1, tree=genome_tree)
beta_div_func_accli<-hillpair(data=accli.counts, q=1, dist=dist)

6.3.2.2 Richness


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)   
Groups     1 0.11796 0.117959 12.963    999  0.002 **
Residuals 25 0.22748 0.009099                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
          Cold_wet Hot_dry
Cold_wet             0.001
Hot_dry  0.0013711        
Df SumOfSqs R2 F Pr(>F)
Population 1 1.639807 0.179834 5.481634 0.001
Residual 25 7.478640 0.820166 NA NA
Total 26 9.118447 1.000000 NA NA

6.3.2.3 Neutral


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.07844 0.078443 5.2384    999  0.022 *
Residuals 25 0.37437 0.014975                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.023
Hot_dry  0.030815        
Df SumOfSqs R2 F Pr(>F)
Population 1 1.947003 0.2306127 7.493387 0.001
Residual 25 6.495736 0.7693873 NA NA
Total 26 8.442739 1.0000000 NA NA

6.3.2.4 Phylogenetic


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.06739 0.067395 2.9532    999  0.105
Residuals 25 0.57052 0.022821                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.106
Hot_dry  0.098068        
Df SumOfSqs R2 F Pr(>F)
Population 1 0.2441653 0.1224638 3.488854 0.019
Residual 25 1.7496100 0.8775362 NA NA
Total 26 1.9937754 1.0000000 NA NA

6.3.2.5 Functional


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.02496 0.024955 0.6729    999  0.446
Residuals 25 0.92714 0.037085                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.435
Hot_dry   0.41979        
Df SumOfSqs R2 F Pr(>F)
Population 1 0.0279454 0.0248037 0.6358634 0.451
Residual 25 1.0987171 0.9751963 NA NA
Total 26 1.1266624 1.0000000 NA NA
beta_q0n_nmds_accli <- beta_div_richness_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

beta_q1n_nmds_accli <- beta_div_neutral_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

beta_q1p_nmds_accli <- beta_div_phylo_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

beta_q1f_nmds_accli <- beta_div_func_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

6.3.3 3. Comparison between Wild and Acclimation

accli1 <- meta  %>%
  filter(time_point == "0_Wild" | time_point == "1_Acclimation")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

accli1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(accli1))]
identical(sort(colnames(accli1.counts)),sort(as.character(rownames(accli1))))

accli1_nmds <- sample_metadata %>%
  filter(time_point == "0_Wild" | time_point == "1_Acclimation")

6.3.3.1 Number of samples used

[1] 54
beta_div_richness_accli1<-hillpair(data=accli1.counts, q=0)
beta_div_neutral_accli1<-hillpair(data=accli1.counts, q=1)
beta_div_phylo_accli1<-hillpair(data=accli1.counts, q=1, tree=genome_tree)
beta_div_func_accli1<-hillpair(data=accli1.counts, q=1, dist=dist)
6.3.3.1.1 Richness

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.05014 0.050145 6.2252    999  0.017 *
Residuals 52 0.41886 0.008055                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                0_Wild 1_Acclimation
0_Wild                          0.02
1_Acclimation 0.015808              
Df SumOfSqs R2 F Pr(>F)
time_point 1 0.6172653 0.0360987 2.320618 0.001
species 1 2.8279677 0.1653842 10.631785 0.001
time_point:species 1 0.3545578 0.0207351 1.332965 0.059
Residual 50 13.2995905 0.7777820 NA NA
Total 53 17.0993812 1.0000000 NA NA
6.3.3.1.2 Neutral

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.0199 0.0199035 2.1213    999  0.155
Residuals 52 0.4879 0.0093827                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               0_Wild 1_Acclimation
0_Wild                        0.162
1_Acclimation 0.15128              
Df SumOfSqs R2 F Pr(>F)
time_point 1 0.9050519 0.0541893 3.792749 0.001
species 1 3.3236300 0.1989999 13.928143 0.001
time_point:species 1 0.5416391 0.0324302 2.269815 0.007
Residual 50 11.9313461 0.7143805 NA NA
Total 53 16.7016671 1.0000000 NA NA
6.3.3.1.3 Phylogenetic

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01334 0.013340 0.6524    999  0.417
Residuals 52 1.06332 0.020449                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               0_Wild 1_Acclimation
0_Wild                        0.411
1_Acclimation 0.42294              
Df SumOfSqs R2 F Pr(>F)
time_point 1 0.2890434 0.0766494 4.956318 0.003
species 1 0.3508889 0.0930498 6.016803 0.001
time_point:species 1 0.2151377 0.0570509 3.689034 0.003
Residual 50 2.9159082 0.7732498 NA NA
Total 53 3.7709782 1.0000000 NA NA
6.3.3.1.4 Functional

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.0123 0.012300 0.4817    999  0.494
Residuals 52 1.3277 0.025533                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               0_Wild 1_Acclimation
0_Wild                          0.5
1_Acclimation 0.49073              
Df SumOfSqs R2 F Pr(>F)
time_point 1 0.0448774 0.0269021 1.4865056 0.233
species 1 0.0973005 0.0583275 3.2229509 0.315
time_point:species 1 0.0165026 0.0098926 0.5466273 0.401
Residual 50 1.5094945 0.9048777 NA NA
Total 53 1.6681751 1.0000000 NA NA
beta_richness_nmds_accli1 <- beta_div_richness_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_accli1 <- beta_div_neutral_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_accli1 <- beta_div_phylo_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = join_by(sample == Tube_code))

beta_func_nmds_accli1 <- beta_div_func_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = join_by(sample == Tube_code))

6.3.4 4. Do the antibiotics work?

6.3.4.1 Acclimation vs antibiotics

treat <- meta  %>%
  filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

treat.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat))]
identical(sort(colnames(treat.counts)),sort(as.character(rownames(treat))))

treat_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")

6.3.4.2 Number of samples used

[1] 50
beta_div_richness_treat<-hillpair(data=treat.counts, q=0)
beta_div_neutral_treat<-hillpair(data=treat.counts, q=1)
beta_div_phylo_treat<-hillpair(data=treat.counts, q=1, tree=genome_tree)
beta_div_func_treat<-hillpair(data=treat.counts, q=1, dist=dist)
6.3.4.2.1 Richness

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq     F N.Perm Pr(>F)  
Groups     1 0.025318 0.0253178 6.021    999  0.015 *
Residuals 48 0.201837 0.0042049                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                       0.017
2_Antibiotics      0.017817              
Df SumOfSqs R2 F Pr(>F)
time_point 1 1.888584 0.0949462 6.361098 0.001
species 1 2.117109 0.1064350 7.130814 0.001
individual 25 9.353701 0.4702455 1.260199 0.002
Residual 22 6.531709 0.3283734 NA NA
Total 49 19.891103 1.0000000 NA NA
6.3.4.2.2 Neutral

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)   
Groups     1 0.039587 0.039587 6.8387    999  0.009 **
Residuals 48 0.277854 0.005789                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                       0.011
2_Antibiotics      0.011886              
Df SumOfSqs R2 F Pr(>F)
time_point 1 2.024181 0.1063620 9.051981 0.001
species 1 2.853103 0.1499183 12.758858 0.001
individual 25 9.234189 0.4852168 1.651783 0.001
Residual 22 4.919584 0.2585029 NA NA
Total 49 19.031057 1.0000000 NA NA
6.3.4.2.3 Phylogenetic

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq Mean Sq      F N.Perm Pr(>F)    
Groups     1 0.58372 0.58372 35.413    999  0.001 ***
Residuals 48 0.79119 0.01648                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                       0.001
2_Antibiotics    2.9795e-07              
Df SumOfSqs R2 F Pr(>F)
time_point 1 1.8065206 0.2113909 18.636551 0.001
species 1 0.7903334 0.0924813 8.153292 0.001
individual 25 3.8164689 0.4465860 1.574869 0.007
Residual 22 2.1325541 0.2495419 NA NA
Total 49 8.5458771 1.0000000 NA NA
6.3.4.2.4 Functional

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.18591 0.185914 5.0679    999  0.022 *
Residuals 48 1.76088 0.036685                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                        0.02
2_Antibiotics      0.028989              
Df SumOfSqs R2 F Pr(>F)
time_point 1 1.8020952 0.3750193 33.6195614 0.001
species 1 0.0031247 0.0006503 0.0582938 0.835
individual 25 1.8208629 0.3789249 1.3587875 0.221
Residual 22 1.1792568 0.2454055 NA NA
Total 49 4.8053396 1.0000000 NA NA
beta_richness_nmds_treat <- beta_div_richness_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_treat <- beta_div_neutral_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_treat <- beta_div_phylo_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = join_by(sample == Tube_code))

beta_func_nmds_treat <- beta_div_func_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = join_by(sample == Tube_code))

6.3.5 5. Does the FMT work?

6.3.5.1 Comparison between FMT2 vs Post-FMT2

#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
  mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)

transplant3<-transplants_metadata%>%
  filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")%>%
  column_to_rownames("newID")

transplant3_nmds <- transplants_metadata %>%
  filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")

full_counts<-temp_genome_counts %>%
    t()%>%
    as.data.frame()%>%
    rownames_to_column("Tube_code")%>%
    full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))

transplant3_counts<-full_counts %>%
  filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2") %>%
  subset(select=-c(315:324)) %>%
  column_to_rownames("newID")%>%
  subset(select=-c(1))%>%
  t() %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)

identical(sort(colnames(transplant3_counts)),sort(as.character(rownames(transplant3))))

6.3.5.2 Number of samples used

[1] 49
beta_div_richness_transplant3<-hillpair(data=transplant3_counts, q=0)
beta_div_neutral_transplant3<-hillpair(data=transplant3_counts, q=1)
beta_div_phylo_transplant3<-hillpair(data=transplant3_counts, q=1, tree=genome_tree)
beta_div_func_transplant3<-hillpair(data=transplant3_counts, q=1, dist=dist)
#Arrange of metadata dataframe
transplant3_arrange<-transplant3[labels(beta_div_neutral_transplant3$S),]
6.3.5.2.1 Richness
Df SumOfSqs R2 F Pr(>F)
species 1 1.180473 0.0855095 5.155229 0.040
time_point 1 0.860906 0.0623612 3.759652 0.001
type 1 1.459433 0.1057165 6.373471 0.003
Residual 45 10.304350 0.7464128 NA NA
Total 48 13.805162 1.0000000 NA NA
                     pairs Df SumsOfSqs  F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 1.4169018 5.739828 0.15622903   0.001      0.003   *
2   Control vs Hot_control  1 2.0940966 8.509112 0.21005427   0.001      0.003   *
3 Treatment vs Hot_control  1 0.3004618 1.265034 0.04179854   0.150      0.450    
6.3.5.2.2 Neutral
Df SumOfSqs R2 F Pr(>F)
species 1 1.2800927 0.0939787 6.068484 0.006
time_point 1 0.9350566 0.0686477 4.432785 0.001
type 1 1.9135997 0.1404879 9.071725 0.001
Residual 45 9.4923500 0.6968858 NA NA
Total 48 13.6210990 1.0000000 NA NA
                     pairs Df SumsOfSqs   F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 1.8758788  8.282671 0.21084796   0.001      0.003   *
2   Control vs Hot_control  1 2.4396317 10.635546 0.24945256   0.001      0.003   *
3 Treatment vs Hot_control  1 0.3158428  1.394345 0.04587515   0.121      0.363    
6.3.5.2.3 Phylogenetic
Df SumOfSqs R2 F Pr(>F)
species 1 0.1400466 0.0952654 5.873615 0.048
time_point 1 0.1138047 0.0774145 4.773017 0.002
type 1 0.1432667 0.0974558 6.008665 0.004
Residual 45 1.0729504 0.7298643 NA NA
Total 48 1.4700683 1.0000000 NA NA
                     pairs Df  SumsOfSqs  F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.14387705 5.735321 0.15612552   0.001      0.003   *
2   Control vs Hot_control  1 0.22715701 9.044894 0.22036587   0.001      0.003   *
3 Treatment vs Hot_control  1 0.04648319 1.704277 0.05550617   0.111      0.333    
6.3.5.2.4 Functional
Df SumOfSqs R2 F Pr(>F)
species 1 0.0092808 0.0077189 0.3741811 0.361
time_point 1 -0.0061674 -0.0051295 -0.2486581 0.869
type 1 0.0831052 0.0691191 3.3506286 0.260
Residual 45 1.1161295 0.9282915 NA NA
Total 48 1.2023481 1.0000000 NA NA
                     pairs Df    SumsOfSqs     F.Model           R2 p.value p.adjusted sig
1     Control vs Treatment  1  0.078539743  4.59293783  0.129040706   0.062      0.186    
2   Control vs Hot_control  1  0.052468954  2.13675422  0.062593948   0.172      0.516    
3 Treatment vs Hot_control  1 -0.002340352 -0.07432315 -0.002569452   0.853      1.000    
beta_richness_nmds_transplant3 <- beta_div_richness_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))

beta_neutral_nmds_transplant3 <- beta_div_neutral_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))

beta_phylo_nmds_transplant3 <- beta_div_phylo_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))

beta_func_nmds_transplant3 <- beta_div_func_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))
p0<-beta_richness_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylo_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_func_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")

6.3.5.3 Comparison between the different experimental time points

6.3.5.3.1 Comparison against Acclimation samples with combined Transplant samples
The estimated time for calculating the 5151 pairwise combinations is 26 seconds.
ggarrange( p1, p2, p3, ncol=3, nrow=1, common.legend = TRUE, legend="right")

6.3.6 6. Are there differences between the control and the treatment group?

6.3.6.1 After 1 week –> Post-FMT1

post1 <- meta %>%
  filter(time_point == "5_Post-FMT1")

post1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post1))]
identical(sort(colnames(post1.counts)),sort(as.character(rownames(post1))))

post1_nmds <- sample_metadata %>%
  filter(time_point == "5_Post-FMT1")

6.3.6.2 Number of samples used

[1] 26
beta_div_richness_post1<-hillpair(data=post1.counts, q=0)
beta_div_neutral_post1<-hillpair(data=post1.counts, q=1)
beta_div_phylo_post1<-hillpair(data=post1.counts, q=1, tree=genome_tree)
beta_div_func_post1<-hillpair(data=post1.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post1_arrange<-post1[labels(beta_div_neutral_post1$S),]
6.3.6.2.1 Richness

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)  
Groups     2 0.017675 0.0088373 2.3825    999  0.095 .
Residuals 23 0.085312 0.0037092                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.0030000     0.663
Hot_control 0.0068795                 0.208
Treatment   0.6248469   0.2084296          
Df SumOfSqs R2 F Pr(>F)
species 1 0.6340254 0.0768024 2.065607 0.004
type 1 0.5615418 0.0680222 1.829461 0.013
Residual 23 7.0597099 0.8551754 NA NA
Total 25 8.2552771 1.0000000 NA NA
                     pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.5615418 1.729004 0.1033537   0.013      0.039   .
2   Control vs Hot_control  1 0.8438429 2.793772 0.1486541   0.001      0.003   *
3 Treatment vs Hot_control  1 0.3734921 1.268929 0.0779971   0.122      0.366    
6.3.6.2.2 Neutral

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.011001 0.0055005 0.6303    999   0.56
Residuals 23 0.200714 0.0087267                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.22800     0.953
Hot_control 0.21166                 0.457
Treatment   0.95468     0.43604          
Df SumOfSqs R2 F Pr(>F)
species 1 0.7907904 0.1076445 3.056657 0.002
type 1 0.6051778 0.0823784 2.339205 0.004
Residual 23 5.9503501 0.8099772 NA NA
Total 25 7.3463184 1.0000000 NA NA
                     pairs Df SumsOfSqs  F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.6051778 2.250849 0.13047758   0.015      0.045   .
2   Control vs Hot_control  1 1.0528902 4.143637 0.20570451   0.001      0.003   *
3 Treatment vs Hot_control  1 0.4150076 1.637268 0.09840968   0.053      0.159    
6.3.6.2.3 Phylogenetic

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00440 0.0021994 0.1369    999  0.902
Residuals 23 0.36941 0.0160614                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.92700     0.658
Hot_control 0.91505                 0.772
Treatment   0.63312     0.73046          
Df SumOfSqs R2 F Pr(>F)
species 1 0.0560850 0.0531376 1.3149967 0.244
type 1 0.0184254 0.0174571 0.4320099 0.816
Residual 23 0.9809570 0.9294053 NA NA
Total 25 1.0554673 1.0000000 NA NA
                     pairs Df  SumsOfSqs   F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.01842535 0.4144162 0.02688498   0.779      1.000    
2   Control vs Hot_control  1 0.05987967 1.7387847 0.09802164   0.113      0.339    
3 Treatment vs Hot_control  1 0.03212966 0.6477782 0.04139746   0.710      1.000    
6.3.6.2.4 Functional

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq     F N.Perm Pr(>F)
Groups     2 0.00400 0.0020014 0.145    999  0.858
Residuals 23 0.31753 0.0138057                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.56500     0.768
Hot_control 0.59817                 0.842
Treatment   0.75141     0.83718          
Df SumOfSqs R2 F Pr(>F)
species 1 0.0024979 0.0033024 0.0900845 0.642
type 1 0.1161466 0.1535542 4.1887855 0.086
Residual 23 0.6377435 0.8431434 NA NA
Total 25 0.7563879 1.0000000 NA NA
                     pairs Df  SumsOfSqs  F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.11614656 4.724791 0.23953568   0.069      0.207    
2   Control vs Hot_control  1 0.05000930 1.704826 0.09629160   0.234      0.702    
3 Treatment vs Hot_control  1 0.01235859 0.423812 0.02747777   0.461      1.000    
beta_richness_nmds_post1 <- beta_div_richness_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))

beta_neutral_nmds_post1 <- beta_div_neutral_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post1 <- beta_div_phylo_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))

beta_functional_nmds_post1 <- beta_div_func_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")

6.3.6.3 After 2 weeks –>Post-FMT2

post2 <- meta %>%
  filter(time_point == "6_Post-FMT2")

post2.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post2))]
identical(sort(colnames(post2.counts)),sort(as.character(rownames(post2))))

post2_nmds <- sample_metadata %>%
  filter(time_point == "6_Post-FMT2")

6.3.6.4 Number of samples used

[1] 27
beta_div_richness_post2<-hillpair(data=post2.counts, q=0)
beta_div_neutral_post2<-hillpair(data=post2.counts, q=1)
beta_div_phylo_post2<-hillpair(data=post2.counts, q=1, tree=genome_tree)
beta_div_func_post2<-hillpair(data=post2.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post2_arrange<-post2[labels(beta_div_neutral_post2$S),]
6.3.6.4.1 Richness

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.002011 0.0010056 0.1982    999  0.839
Residuals 24 0.121775 0.0050740                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.70700     0.822
Hot_control 0.67789                 0.595
Treatment   0.79246     0.59820          
Df SumOfSqs R2 F Pr(>F)
type 2 1.504341 0.1967776 2.939822 0.001
Residual 24 6.140538 0.8032224 NA NA
Total 26 7.644879 1.0000000 NA NA
                     pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
1     Treatment vs Control  1 0.6463814 2.560441 0.1379515   0.001      0.003   *
2 Treatment vs Hot_control  1 0.4796256 1.916520 0.1069694   0.001      0.003   *
3   Control vs Hot_control  1 1.1305044 4.268317 0.2105906   0.001      0.003   *
6.3.6.4.2 Neutral

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.008262 0.0041311 0.8024    999  0.459
Residuals 24 0.123559 0.0051483                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.44800     0.667
Hot_control 0.44675                 0.252
Treatment   0.65989     0.25095          
Df SumOfSqs R2 F Pr(>F)
type 2 1.923807 0.2603795 4.224537 0.001
Residual 24 5.464666 0.7396205 NA NA
Total 26 7.388473 1.0000000 NA NA
                     pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
1     Treatment vs Control  1 1.0227481 4.648335 0.2251191   0.001      0.003   *
2 Treatment vs Hot_control  1 0.5010202 2.206532 0.1211945   0.002      0.006   *
3   Control vs Hot_control  1 1.3619424 5.771031 0.2650785   0.001      0.003   *
6.3.6.4.3 Phylogenetic

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.000407 0.0002034 0.0487    999  0.957
Residuals 24 0.100305 0.0041794                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.93200     0.832
Hot_control 0.93765                 0.758
Treatment   0.83933     0.76015          
Df SumOfSqs R2 F Pr(>F)
type 2 0.1594363 0.2042241 3.079623 0.001
Residual 24 0.6212564 0.7957759 NA NA
Total 26 0.7806927 1.0000000 NA NA
                     pairs Df  SumsOfSqs  F.Model        R2 p.value p.adjusted sig
1     Treatment vs Control  1 0.05927454 2.382025 0.1295845   0.032      0.096    
2 Treatment vs Hot_control  1 0.06906280 2.722460 0.1454115   0.007      0.021   .
3   Control vs Hot_control  1 0.11081709 4.043656 0.2017424   0.003      0.009   *
6.3.6.4.4 Functional

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.01126 0.0056302 0.2861    999  0.805
Residuals 24 0.47233 0.0196806                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.51700     0.668
Hot_control 0.48255                 0.817
Treatment   0.60116     0.75643          
Df SumOfSqs R2 F Pr(>F)
type 2 -0.0038724 -0.0056213 -0.0670788 0.921
Residual 24 0.6927468 1.0056213 NA NA
Total 26 0.6888744 1.0000000 NA NA
                     pairs Df    SumsOfSqs     F.Model           R2 p.value p.adjusted sig
1     Treatment vs Control  1 -0.008527330 -0.46290555 -0.029793572   0.858          1    
2 Treatment vs Hot_control  1 -0.001648721 -0.04717131 -0.002956924   0.903          1    
3   Control vs Hot_control  1  0.004367477  0.13147026  0.008149924   0.693          1    
beta_richness_nmds_post2 <- beta_div_richness_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))

beta_neutral_nmds_post2 <- beta_div_neutral_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post2 <- beta_div_phylo_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))

beta_functional_nmds_post2 <- beta_div_func_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")